In real world forecasting task, we don’t have luxury of actuals in hand for better model selection, in such realistic situations, forecast stability can guide us to some extent. Forecast Stability in simple terms, is all about how forecasts behave versus forecasts, we can measure it with simple coefficient of variation. This measure also helps us to understand non-randomness across the data. When we have data at SKU (Store Keep Unit) Level, looking at it regularly provides some extra information that can be used for correcting non-randomness, especially for low volume SKUs. In this notebook exercise, I have three consecutive weeks data for 50 SKUs actuals and presented forecasts, to demonstrate what we can deduce from regular observation of forecast stability with said simple measure.
PS: Demonstration is based on weekly model forecasts, which are either different or same models across weeks based on a selection procedure. Current demonstration selects models based on minimal error across the different time series models namely., ARIMA, SARIMA and ETS based on R package “forecast”.
Below link has the data and R notebook:
Happy R Programming!